3D printing-NFC hardware and self-research website intelligent content adaptation system based on end-cloud collaboration

Abstract

The invention discloses a 3D printing-NFC hardware and self-research website intelligent content adaptation system based on end-cloud collaboration, belongs to the field of fusion of the Internet of Things and the cultural and creative technology, relates to 3D printing, NFC communication and end-cloud collaboration technologies, and is suitable for scenes such as scenic spot cultural and creative guide and industrial miniature identification. The problem to be solved is to solve the problems of adaptive splitting of 3D printing-NFC cultural and creative product hardware and a website, offline content missing and version asynchronization. According to the technical scheme, the hardware is of a degradable 3D printing structure of 12.5 mm * 9.4 mm * 3 mm, and an NTG chip with 1-byte characteristic codes (recording chips, functions and firmware) and a 25% cache region is arranged in the 3D printing structure; the website is provided with an analysis module, an adaptation module, a synchronization module and a version module, differentiated content is pushed according to hardware, and offline caching, incremental updating and firmware upgrading 1s synchronization are supported; the cooperative total delay is less than or equal to 300ms. The method is mainly used for scenic spot cultural and creative application and the like, the multi-chip adaptation success rate is 99.5%, the network-free content integrity is larger than or equal to 80%, the version synchronization delay is smaller than or equal to 1 s, Android 8.0 and iOS 13.0 or above are compatible, and hardware is degradable.

Description

Technical Field

[0001] This invention belongs to the field of integration of Internet of Things and cultural and creative technologies. Specifically, it relates to an intelligent system based on an edge-cloud collaborative architecture that realizes 3D printing-NFC hardware feature recognition and dynamic adaptation of self-developed website content. It is especially suitable for multi-scenario interactive adaptation of miniaturized cultural and creative products. Background Technology

[0002] In the field of "physical-digital" interaction of 3D printing-NFC cultural and creative products, existing technologies have the following significant shortcomings:

[0003] Poor real-time adaptability: In traditional solutions, websites use fixed content templates and do not consider hardware characteristics (such as the storage difference between NTAG213 chip (144 bytes) and NTAG215 chip (504 bytes)). When loading large-capacity 3D models with small-capacity chips, the failure rate exceeds 30%, and the content parsing latency on edge devices exceeds 200ms, which cannot meet the real-time interaction requirements.

[0004] Insufficient robustness in complex scenarios: When there is no network, hardware firmware upgrades, or multiple chip models are used, the website has no adaptive adjustment logic, the completeness of offline scene content display is 0, the function activation rate after firmware upgrade is only 35%, and the multi-chip adaptation miss rate reaches 28%.

[0005] The collaborative logic is too simple: existing methods have not established a two-way data interaction mechanism between hardware and website. For example, Chinese patent CN202502072U only realizes the NFC chip to trigger web page jump, without involving content adaptation; the NFC content push scheme proposed by US patent US113456789B2 adopts a fixed caching strategy, which cannot be dynamically adjusted according to hardware capacity, and the cache utilization rate is less than 40%.

[0006] Purpose of the invention

[0007] This invention provides a high-precision, low-latency 3D printing-NFC hardware and self-developed website-cloud collaborative adaptation technology. Through hardware feature encoding, cloud adaptive engine and bidirectional collaborative process, it solves the problems of poor real-time adaptation, insufficient robustness in complex scenarios and simple collaborative logic in existing technologies.

[0008] Technical solution

[0009] The core technical solution of this invention is as follows, and its specific limitations correspond to claims 1-8:

[0010] 1. 3D Printing - NFC Hardware Design

[0011] Constructing a micro-hardware architecture that includes feature code storage and dynamic caching, specifically including:

[0012] Hardware main body: Made of corn starch-based biodegradable material by FDM 3D printing, with an overall size of 12.5mm×9.4mm×3mm (fitting the iPhone 15 Pro Max camera area). It has a built-in NTAG series NFC chip that supports the ISO14443A protocol. The chip is connected to external functional modules (optional pressure sensor, RGB LED) through a flexible PCB board.

[0013] Feature code storage: A 1-byte (8-bit) feature code storage area is reserved in the NFC chip. The first 2 bits identify the chip type (01 represents NTAG213, 10 represents NTAG215, and 11 represents NTAG216), the middle 2 bits identify the functional module configuration (00 represents no module, 01 represents with pressure sensor, and 10 represents with RGB LED), and the last 4 bits identify the firmware version (0001 represents V1.0 and 0010 represents V2.0).

[0014] Dynamic cache area: The offline cache area is divided into 25% of the total storage capacity of the chip. For example, 40 bytes of cache space are allocated to the NTAG213 chip (144 bytes) and 150 bytes of cache space are allocated to the NTAG215 chip (504 bytes). The cached data is compressed using the LZMA algorithm (compression rate not less than 60%) to ensure that more content is loaded within the limited storage.

[0015] 2. Self-developed website adaptive engine

[0016] Build a cloud engine that includes feature parsing, content adaptation, offline synchronization, and version management, specifically including:

[0017] Feature parsing module: Reads hardware feature codes through Web NFC API, calls the preset chip parameter database (including basic information such as storage capacity and communication rate of NTAG series chips), parses out the chip type, function configuration and firmware version of the hardware, and determines the adaptation priority according to the hardware characteristics (high-capacity chips and hardware with functional modules have higher priority).

[0018] Content adaptation module: Pushes differentiated content based on hardware storage capacity—for 144-byte level chips (such as NTAG213), it pushes simplified 3D models with no more than 1000 triangles (file size ≤ 50KB) and concise text of no more than 80 characters; for 504-byte level chips (such as NTAG215), it pushes standard 3D models (file size ≤ 200KB) and complete text; if the hardware has a pressure sensor, it synchronously loads a "press interaction menu", with different pressing pressure (0.3-0.8N) corresponding to different content display;

[0019] Offline synchronization module: Data synchronization is achieved using Service Worker technology. When a user connects to the network for the first time to sense the hardware, the website automatically pushes the appropriate offline content package to the hardware cache. In offline mode, the hardware directly reads the cached content after sensing the network. When the network is connected again, the website only updates the content differences, reducing the amount of data transmission.

[0020] Version Management Module: Establish a mapping library of "hardware feature code - website function version". When the hardware firmware is upgraded (such as from V1.0 to V2.0), the website updates the corresponding adaptation logic within 1 second and automatically blocks high-power-consuming functions (such as 4K model rendering) for old hardware versions to avoid functional abnormalities.

[0021] 3. End-to-Cloud Collaborative Interaction Process

[0022] To achieve two-way data interaction between hardware, mobile devices, and websites, the specific steps are as follows:

[0023] ① The mobile device senses the 3D printing-NFC hardware through the NFC module and reads the hardware feature code and buffer status (time ≤ 50ms);

[0024] ② The mobile device uploads the read data to the self-developed website cloud via HTTPS protocol (transmission latency ≤30ms);

[0025] ③ The cloud feature parsing module generates adaptation instructions, and the content adaptation module builds a personalized content package based on the instructions (time ≤ 20ms);

[0026] ④ If the device is online, the website will directly push the complete content to the mobile device; if the device is offline, the mobile device will access the content from the hardware cache (loading delay ≤ 100ms).

[0027] ⑤ During the interaction, the mobile device uploads hardware sensor data (such as the pressure value of the pressure sensor) in real time, and the website dynamically adjusts the displayed content and feedback instructions based on the data (response delay ≤ 50ms). Attached Figure Description

[0028] Figure 1 3D Printed NFC Hardware Structure Cross-Sectional Diagram, Label 1 - Biodegradable Shell (12.5mm × 9.4mm ×

[0029] 3mm), 2-NFC chip, 3-Feature code storage area, 4-Dynamic cache area, 5-Flexible PCB board, 6

[0030] - Location and connection of the pressure sensor;

[0031] Figure 2The self-developed website adaptive engine architecture diagram shows the functional division and data flow of the following modules: 1-Feature parsing module, 2-Content adaptation module, 3-Offline synchronization module, and 4-Version management module.

[0032] Figure 3 : End-to-cloud collaborative interaction flowchart, marking the 5 core steps from hardware sensing to content display and the time consumed in each step;

[0033] Figure 4 The experimental data comparison chart presents the comparison results between the present invention and existing technologies in three indicators: adaptation success rate, content loading delay, and offline content completeness. Detailed Implementation

[0034] Example 1: Hardware Preparation and Website Deployment

[0035] 1. Hardware preparation 3D printing: Anycubic Kobra Neo 3D printer was used, with a layer thickness of 0.1mm and an infill rate of 50%. PLA-corn starch composite biodegradable resin was used to print the shell (degradation period ≤ 6 months);

[0036] Chip assembly: The NTAG213 chip is implanted, and the characteristic code "01000001" is written using an ST-Link programmer.

[0037] (Representing NTAG213, no functional module, V1.0 firmware), and allocate a 40-byte buffer area; if a pressure sensor needs to be installed, select a 0.5mm×0.5mm piezoresistive sensor, connect its pin to pin 4 of the chip, and update the feature code to "01010001".

[0038] Performance testing: Perform 1000 sensing tests on the completed hardware, with a sensing success rate of ≥99.5% and a cache data read / write speed of ≥100 bytes / ms.

[0039] 2. Website Deployment

[0040] Cloud server: Alibaba Cloud ECS instance (configured with 2 cores and 4GB RAM, CentOS 7.9 system), deployed with Nginx reverse proxy and Redis caching to improve website response speed;

[0041] Engine Development: The feature parsing module is developed based on Python 3.9 (integrating the nfcpy library to implement NFC data reading), the content adaptation module uses the Three.js framework to implement lightweight processing of 3D models, and the offline synchronization module is developed based on the ServiceWorker API;

[0042] Stress test: Simulate 1000 concurrent requests using JMeter, website response success rate ≥99.8%, average response latency ≤150ms.

[0043] Example 2: Real-time Adaptation Process Verification

[0044] 1. Test Environment

[0045] Test hardware: 10 3D printed samples of each of the NTAG213 (144 bytes) and NTAG215 (504 bytes) chips;

[0046] Test equipment: iPhone 15 Pro Max (iOS 17.0 system), Jetson Nano edge computing device;

[0047] Test scenarios: Online (50Mbps network), Offline (no network), Hardware firmware upgrade (V1.0 →

[0048] V2.0).

[0049] Test steps and results

[0050] 2. Online scenarios:

[0051] Using an iPhone 15 Pro Max to sense the NTAG215 hardware, the website pushed a 200KB ancient building 3D model+.

[0052] A 200-word explanatory text, with a loading delay of 85ms, achieves a 100% success rate in adaptation.

[0053] Offline scenario: After disconnecting from the network, the NTAG213 hardware is sensed, and the mobile device reads a 50KB simplified model and 80 words of explanatory text from the cache, with a content display completeness of 82%.

[0054] Firmware upgrade scenario: Upgrading the NTAG213 hardware firmware from V1.0 to V2.0 (feature code updated to...)

[0055] The website updated its adaptation logic within 0.8 seconds ("01000010") and successfully enabled the newly added RGB LED control function.

[0056] Experimental data and beneficial effects

[0057] 1. Improved real-time performance: Total latency for edge-cloud collaboration is ≤300ms, a 45.5% reduction compared to the traditional fixed template solution (550ms); content parsing speed on edge devices (Jetson Nano) reaches 35FPS, a 94.4% improvement compared to the CN202502072U solution (18FPS);

[0058] 2. Optimized compatibility: The compatibility success rate for multiple chips (NTAG213 / 215 / 216) is 99.5%, which is 38.2% higher than the US113456789B2 solution (72%); the false negative rate in complex scenarios (no network, firmware upgrade) is reduced to 3.2%, which is 88.6% lower than the existing technology (28%).

[0059] 3. Enhanced offline availability: Content display completeness in offline scenarios is ≥80%, an improvement of 80 percentage points compared to solutions that can only read chip UIDs (0%); cache utilization reaches 78%, an improvement of 95% compared to fixed cache solutions (40%);

[0060] 4. Improved version compatibility: After hardware firmware upgrade, the function activation rate is 100%, and the synchronization delay is ≤1s, which is 96.7% lower than the manual configuration scheme (30s).

[0061] Advantages of the invention

[0062] 1. Strong dynamic adaptability: Through feature code encoding and priority judgment, it achieves accurate adaptation to multiple hardware models with a success rate of over 99%, solving the compatibility problem of fixed templates;

[0063] 2. High robustness in various scenarios: Offline caching and version coordination mechanisms ensure that the functional availability rate exceeds 99% in scenarios without network access and during firmware upgrades, which is 64 percentage points higher than existing technologies;

[0064] 3. Excellent engineering practicality: The hardware uses biodegradable materials and is compatible with existing 3D printing production lines; the website supports...

[0065] For Android 8.0 / iOS 13.0 and above, power consumption is reduced by 70% compared to pure cloud solutions, making it suitable for edge device deployment.

[0066] Reference relationship of claims

[0067] The specific embodiments described herein fully support all the technical features of claims 1-8, wherein:

[0068] Example 1 corresponds to claim 1 (3D printing-NFC hardware structure), claim 2 (self-developed website adaptive engine), and claim 3 (hardware preparation and website deployment scheme);

[0069] Example 2 corresponds to claim 4 (end-to-cloud collaborative interaction process), claim 5 (content adaptation logic), and claim 6 (multi-scenario adaptation verification);

[0070] The experimental data section supports claims 7 (system performance indicators) and 8 (system application scenarios).

Claims

1. A 3D printing-NFC hardware and self-developed website intelligent content adaptation system based on edge-cloud collaboration, characterized in that, include: The 3D-printed NFC hardware is made of corn starch-based biodegradable material and is formed by FDM 3D printing. The overall size is 12.5mm×9.4mm×3mm. It has a built-in NTAG series NFC chip that supports the ISO14443A protocol. The chip reserves a 1-byte feature code storage area and a dynamic cache area. The feature code storage area records the chip type, functional module configuration and firmware version information. The dynamic cache area is divided into 25% of the total chip capacity. The self-developed website includes a feature parsing module, a content adaptation module, an offline synchronization module, and a version management module. The feature parsing module reads hardware feature codes and parses hardware parameters through the Web NFC API. The content adaptation module pushes differentiated content based on hardware parameters. The offline synchronization module enables offline access to cached content and online incremental updates. The version management module establishes a mapping relationship between hardware feature codes and website function versions. The edge-cloud collaborative interaction process involves the mobile device sensing the hardware and uploading data to the website. The website then generates an adapted content package and pushes it to the mobile device. During the interaction, the hardware sensing data and website feedback instructions are synchronized in real time.

2. The system according to claim 1, characterized in that, The feature code storage area uses 8-bit binary encoding. The first 2 bits identify the chip type (01 = NTAG213, 10 = NTAG215, 11 = NTAG216), the middle 2 bits identify the functional module (00 = no module, 01 = pressure sensor, 10 = RGB LED), and the last 4 bits identify the firmware version (0001 = V1.0, 0010 = V2.0).

3. The system according to claim 1, characterized in that, In the dynamic cache area, the NTAG213 chip (144 bytes) is allocated 40 bytes of cache space, and the NTAG215 chip (504 bytes) is allocated 150 bytes of cache space. The cached data is compressed using the LZMA algorithm with a compression rate of ≥60%.

4. The system according to claim 1, characterized in that, The differentiated push rules of the content adaptive module are as follows: for 144-byte level chips, a simplified 3D model (≤50KB) with ≤1000 triangles and text of no more than 80 characters is pushed; for 504-byte level chips, a standard 3D model (≤200KB) and complete text are pushed; when the pressure sensing module is detected, an interactive menu associated with the pressure level (0.3-0.8N) is loaded.

5. The system according to claim 1, characterized in that, The offline synchronization module uses Service Worker technology. When the network is connected for the first time, it pushes an adaptation cache package. In the offline state, it calls the cached content. After the network is connected, it only updates the content differences. The synchronization delay is ≤1 second.

6. The system according to claim 1, characterized in that, The version management module updates the adaptation logic within 1 second after the hardware firmware is upgraded, and automatically blocks high-power-consuming functions (4K model rendering, multi-track audio, etc.) for old version hardware.

7. The system according to claim 1, characterized in that, The total latency of the edge-cloud collaborative interaction process is ≤300ms, of which hardware sensing time is ≤50ms, data upload latency is ≤30ms, website parsing and content generation time is ≤20ms, content loading latency is ≤100ms, and interaction response latency is ≤50ms.

8. The system according to claim 1, characterized in that, It can be applied to scenic area cultural and creative tours, personalized gift interactions, or industrial micro-signage scenarios. The hardware is compatible with the camera area of ​​mobile devices such as iPhone 15 Pro Max, and the website supports Android 8.0 and iOS 13.0 and above.

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